Table REF  presents the performance comparison of different recommendation methods.
On the largest dataset Yelp with approximately 1.3 million records, our model PETER performs as good as the three competitive baselines (i.e., SVD++, NRT and NETE), which shows the rationale of our recommendation module.
Since our model PETER has more parameters to learn, it may underfit on small datasets.
This explains why it does not always perform the best on TripAdvisor and Amazon.
When more training data are available to Transformer, usually the performance will become better, as evidenced by GPT-2 [1]} and GPT-3 [2]}.
Thus, we can expect our model to perform well in real-world applications, where the training data are bigger than the testing datasets, e.g., billion-scale users in Amazon.
